Abstract
K-Means is one of the popular methods for generating clusters. It is very well-known and commonly used for its convenience and fastness. The main disadvantage of these criteria is that user should specify the number of cluster in enhance. As a repetitive clustering strategy, a K-Means criterion is very delicate to the preliminary beginning circumstances. In this paper, has been proposed a clustering strategy known as Multi-dimensional K-Means clustering criteria. This algorithm auto generates preliminary k (the preferred variety of cluster) without asking input from the user. It also used a novel strategy of establishing the preliminary centroids. The experiment of the proposed strategy has been conducted using synthetic data, which is taken form LIyod’s K-means experiments. The algorithm is suited for higher education for calculating the student’s CGPA and extracurricular activities with graphs.
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Acknowledgements
This work was supported by Fundamental Research Grant Scheme (FRGS- RDU110104), University Malaysia Pahang under the project “A new Design of Multiple Dimensions Parameter less Data Clustering Technique (Max D-K means) based on Maximum Distance of Data point and LIoyd k-means Algorithm”.
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Mohd, W.M.W., Beg, A.H., Herawan, T., Noraziah, A., Chiroma, H. (2019). Multi-dimensional K-Means Algorithm for Student Clustering. In: Abawajy, J., Othman, M., Ghazali, R., Deris, M., Mahdin, H., Herawan, T. (eds) Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) . Lecture Notes in Electrical Engineering, vol 520. Springer, Singapore. https://doi.org/10.1007/978-981-13-1799-6_14
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DOI: https://doi.org/10.1007/978-981-13-1799-6_14
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